Scalable Graph Processing Frameworks: A Taxonomy and Open Challenges

TLDR: A taxonomy of graph processing systems is proposed and existing systems are mapped to this classification, which captures the diversity in programming and computation models, runtime aspects of partitioning and communication, both for in-memory and distributed frameworks.

Abstract: The world is becoming a more conjunct place and the number of data sources such as social networks, online transactions, web search engines, and mobile devices is increasing even more than had been predicted. A large percentage of this growing dataset exists in the form of linked data, more generally, graphs, and of unprecedented sizes. While today's data from social networks contain hundreds of millions of nodes connected by billions of edges, inter-connected data from globally distributed sensors that forms the Internet of Things can cause this to grow exponentially larger. Although analyzing these large graphs is critical for the companies and governments that own them, big data tools designed for text and tuple analysis such as MapReduce cannot process them efficiently. So, graph distributed processing abstractions and systems are developed to design iterative graph algorithms and process large graphs with better performance and scalability. These graph frameworks propose novel methods or extend previous methods for processing graph data. In this article, we propose a taxonomy of graph processing systems and map existing systems to this classification. This captures the diversity in programming and computation models, runtime aspects of partitioning and communication, both for in-memory and distributed frameworks. Our effort helps to highlight key distinctions in architectural approaches, and identifies gaps for future research in scalable graph systems.